Deep learning models are structurally different from traditional machine learning models and have dramatically more parameters. Deep learning models are always substantially more complex than traditional models. Therefore, the previous methods of model complexity of traditional machine learning models cannot be directly applied to deep learning models to obtain valid complexity measures. For example, measuring the complexity of a decision tree by the depth of the tree [1]}, [2]} and the number of leaf nodes [3]}, [4]} is obviously not applicable to deep learning models. Measuring model complexity by the number of trainable parameters [5]} has a very limited effect on deep learning models since deep learning models are often over-parameterized.
